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向项目添加数据集
您可以向现有项目添加训练数据集或测试数据集。如果要替换现有数据集，请先将现有数据集删除。有关更多信息，请参阅删除数据集。然后，添加新的数据集。
向项目添加数据集（控制台）
您可以使用 Amazon Rekognition Custom Labels 控制台向项目添加训练或测试数据集。
向项目添加数据集
通过以下网址打开 Amazon Rekognition 控制台：https://console.aws.amazon.com/rekognition/。
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在左侧窗格中，选择使用自定义标签。随后将显示 Amazon Rekognition Custom Labels 登录页面。
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在左侧导航窗格中，选择项目。随后将显示“项目”视图。
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选择要添加数据集的项目。
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在左侧导航窗格中，于项目名称下选择数据集。
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如果项目没有现有数据集，则会显示创建数据集页面。执行以下操作：
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在创建数据集页面上，输入图像源信息。有关更多信息，请参阅使用图像创建训练和测试数据集。
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选择创建数据集以创建数据集。
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如果项目已有数据集（训练或测试），则会显示项目详细信息页面。执行以下操作：
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在项目详细信息页面上，选择操作。
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如果想添加训练数据集，请选择创建训练数据集。
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如果想添加训练数据集，请选择创建训练数据集。
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在创建数据集页面上，输入图像源信息。有关更多信息，请参阅使用图像创建训练和测试数据集。
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选择创建数据集以创建数据集。
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向数据集中添加图像。有关更多信息，请参阅添加更多图像（控制台）。
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向数据集中添加标签。有关更多信息，请参阅添加新标签（控制台）。
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为图像添加标签。如果要添加图像级标签，请参阅为图像分配图像级标签。如果要添加边界框，请参阅使用边界框标注物体。有关更多信息，请参阅确定数据集用途。
向项目添加数据集 (SDK)
您可以通过以下方式向现有项目添加训练或测试数据集：
向项目添加数据集 (SDK)
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安装并配置 AWS CLI 和 AWS SDK（如果尚未如此）。有关更多信息，请参阅步骤 4：设置 AWS CLI 和 AWS 软件开发工具包。
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使用以下示例向数据集添加 JSON 行。
- CLI
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将 project_arn 替换为要添加数据集的项目。将 dataset_type 替换为 TRAIN 可创建训练数据集，替换为 TEST 可创建测试数据集。
aws rekognition create-dataset --project-arn project_arn \
--dataset-type dataset_type \
--profile custom-labels-access
- Python
-
使用以下代码创建数据集。提供以下命令行选项：
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
import argparse
import logging
import time
import boto3
from botocore.exceptions import ClientError
logger = logging.getLogger(__name__)
def create_empty_dataset(rek_client, project_arn, dataset_type):
"""
Creates an empty Amazon Rekognition Custom Labels dataset.
:param rek_client: The Amazon Rekognition Custom Labels Boto3 client.
:param project_arn: The ARN of the project in which you want to create a dataset.
:param dataset_type: The type of the dataset that you want to create (train or test).
"""
try:
#Create the dataset.
logger.info("Creating empty %s dataset for project %s",
dataset_type, project_arn)
dataset_type=dataset_type.upper()
response = rek_client.create_dataset(
ProjectArn=project_arn, DatasetType=dataset_type
)
dataset_arn=response['DatasetArn']
logger.info("dataset ARN: %s", dataset_arn)
finished=False
while finished is False:
dataset=rek_client.describe_dataset(DatasetArn=dataset_arn)
status=dataset['DatasetDescription']['Status']
if status == "CREATE_IN_PROGRESS":
logger.info(("Creating dataset: %s ", dataset_arn))
time.sleep(5)
continue
if status == "CREATE_COMPLETE":
logger.info("Dataset created: %s", dataset_arn)
finished=True
continue
if status == "CREATE_FAILED":
error_message = f"Dataset creation failed: {status} : {dataset_arn}"
logger.exception(error_message)
raise Exception(error_message)
error_message = f"Failed. Unexpected state for dataset creation: {status} : {dataset_arn}"
logger.exception(error_message)
raise Exception(error_message)
return dataset_arn
except ClientError as err:
logger.exception("Couldn't create dataset: %s", err.response['Error']['Message'])
raise
def add_arguments(parser):
"""
Adds command line arguments to the parser.
:param parser: The command line parser.
"""
parser.add_argument(
"project_arn", help="The ARN of the project in which you want to create the empty dataset."
)
parser.add_argument(
"dataset_type", help="The type of the empty dataset that you want to create (train or test)."
)
def main():
logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
try:
# Get command line arguments.
parser = argparse.ArgumentParser(usage=argparse.SUPPRESS)
add_arguments(parser)
args = parser.parse_args()
print(f"Creating empty {args.dataset_type} dataset for project {args.project_arn}")
# Create the empty dataset.
session = boto3.Session(profile_name='custom-labels-access')
rekognition_client = session.client("rekognition")
dataset_arn=create_empty_dataset(rekognition_client,
args.project_arn,
args.dataset_type.lower())
print(f"Finished creating empty dataset: {dataset_arn}")
except ClientError as err:
logger.exception("Problem creating empty dataset: %s", err)
print(f"Problem creating empty dataset: {err}")
except Exception as err:
logger.exception("Problem creating empty dataset: %s", err)
print(f"Problem creating empty dataset: {err}")
if __name__ == "__main__":
main()
- Java V2
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使用以下代码创建数据集。提供以下命令行选项：
/*
Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
SPDX-License-Identifier: Apache-2.0
*/
package com.example.rekognition;
import software.amazon.awssdk.auth.credentials.ProfileCredentialsProvider;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.CreateDatasetRequest;
import software.amazon.awssdk.services.rekognition.model.CreateDatasetResponse;
import software.amazon.awssdk.services.rekognition.model.DatasetDescription;
import software.amazon.awssdk.services.rekognition.model.DatasetStatus;
import software.amazon.awssdk.services.rekognition.model.DatasetType;
import software.amazon.awssdk.services.rekognition.model.DescribeDatasetRequest;
import software.amazon.awssdk.services.rekognition.model.DescribeDatasetResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import java.net.URI;
import java.util.logging.Level;
import java.util.logging.Logger;
public class CreateEmptyDataset {
public static final Logger logger = Logger.getLogger(CreateEmptyDataset.class.getName());
public static String createMyEmptyDataset(RekognitionClient rekClient, String projectArn, String datasetType)
throws Exception, RekognitionException {
try {
logger.log(Level.INFO, "Creating empty {0} dataset for project : {1}",
new Object[] { datasetType.toString(), projectArn });
DatasetType requestDatasetType = null;
switch (datasetType) {
case "train":
requestDatasetType = DatasetType.TRAIN;
break;
case "test":
requestDatasetType = DatasetType.TEST;
break;
default:
logger.log(Level.SEVERE, "Unrecognized dataset type: {0}", datasetType);
throw new Exception("Unrecognized dataset type: " + datasetType);
}
CreateDatasetRequest createDatasetRequest = CreateDatasetRequest.builder().projectArn(projectArn)
.datasetType(requestDatasetType).build();
CreateDatasetResponse response = rekClient.createDataset(createDatasetRequest);
boolean created = false;
//Wait until updates finishes
do {
DescribeDatasetRequest describeDatasetRequest = DescribeDatasetRequest.builder()
.datasetArn(response.datasetArn()).build();
DescribeDatasetResponse describeDatasetResponse = rekClient.describeDataset(describeDatasetRequest);
DatasetDescription datasetDescription = describeDatasetResponse.datasetDescription();
DatasetStatus status = datasetDescription.status();
logger.log(Level.INFO, "Creating dataset ARN: {0} ", response.datasetArn());
switch (status) {
case CREATE_COMPLETE:
logger.log(Level.INFO, "Dataset created");
created = true;
break;
case CREATE_IN_PROGRESS:
Thread.sleep(5000);
break;
case CREATE_FAILED:
String error = "Dataset creation failed: " + datasetDescription.statusAsString() + " "
+ datasetDescription.statusMessage() + " " + response.datasetArn();
logger.log(Level.SEVERE, error);
throw new Exception(error);
default:
String unexpectedError = "Unexpected creation state: " + datasetDescription.statusAsString() + " "
+ datasetDescription.statusMessage() + " " + response.datasetArn();
logger.log(Level.SEVERE, unexpectedError);
throw new Exception(unexpectedError);
}
} while (created == false);
return response.datasetArn();
} catch (RekognitionException e) {
logger.log(Level.SEVERE, "Could not create dataset: {0}", e.getMessage());
throw e;
}
}
public static void main(String args[]) {
String datasetType = null;
String datasetArn = null;
String projectArn = null;
final String USAGE = "\n" + "Usage: " + "<project_arn> <dataset_type>\n\n" + "Where:\n"
+ " project_arn - the ARN of the project that you want to add copy the datast to.\n\n"
+ " dataset_type - the type of the empty dataset that you want to create (train or test).\n\n";
if (args.length != 2) {
System.out.println(USAGE);
System.exit(1);
}
projectArn = args[0];
datasetType = args[1];
try {
// Get the Rekognition client
RekognitionClient rekClient = RekognitionClient.builder()
.credentialsProvider(ProfileCredentialsProvider.create("custom-labels-access"))
.region(Region.US_WEST_2)
.build();
// Create the dataset
datasetArn = createMyEmptyDataset(rekClient, projectArn, datasetType);
System.out.println(String.format("Created dataset: %s", datasetArn));
rekClient.close();
} catch (RekognitionException rekError) {
logger.log(Level.SEVERE, "Rekognition client error: {0}", rekError.getMessage());
System.exit(1);
} catch (Exception rekError) {
logger.log(Level.SEVERE, "Error: {0}", rekError.getMessage());
System.exit(1);
}
}
}
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向数据集中添加图像。有关更多信息，请参阅添加更多图像 (SDK)。
